Application of the Systems Chemistry Approach on the Ammonolysis of 1-Ethoxycarbonyl- and 1-Phenoxycarbonyl-3-(2-thienyl)oxindoles. A Method to Predict Reactivity
Bibliographic record
Abstract
The routine prediction of the reactivity of a complex, multifunctional molecule is a challenging and time-consuming procedure. In the last step of the synthesis of the well-known drug substance tenidap, a nonexpected difference was observed between the reactivities of two closely related carbamate moieties, the N-ethoxycarbonyl and the N-phenoxycarbonyl group. A detailed kinetic study, necessitating a significant computational effort, is described in the present paper for this reaction step. On the other hand, the systems chemistry concept, by analyzing the details of the electronic structure and the connections between functional groups in a fast and simple way, is also able to answer this question using various "-icity" parameters (aromaticity, carbonylicity, olefinicity). The complete systems chemistry approach involves all these conjugativicity parameters, while its further simplified version is based on only one key parameter, which is carbonylicity in the present case. The above methods were compared in terms of their predictive power. The results show that the systems chemistry concept, even its one-parameter version, is applicable for the characterization of this challenging reactivity issue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".